class_docstr: Handle a linear transform, like torch.nn.Linear_, and a given activation, in a single kernel.
  The whole transform: is :math:`y = activation(xA^T + b)`.
  
  This is typically significantly faster than PyTorch while using fp16 and non-sigmoid activations,
  as of September 2021.
  
  .. _torch.nn.Linear: https://pytorch.org/docs/stable/generated/torch.nn.Linear.html
class_docstr_tok: ["Handle","a","linear","transform",",","like","torch.nn.Linear_",",","and","a","given","activation",",","in","a","single","kernel",".","The","whole","transform",":","is",":","math",":","`","y","=","activation","(","xA^T","+","b",")","`",".","This","is","typically","significantly","faster","than","PyTorch","while","using","fp16","and","non-sigmoid","activations",",","as","of","September","2021",".","..","_torch.nn.Linear",":","https",":","\/\/pytorch.org\/docs\/stable\/generated\/torch.nn.Linear.html"]
code: def reset_parameters(self) -> None:
      torch.nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
      if self.bias is not None:
          fan_in, _ = torch.nn.init._calculate_fan_in_and_fan_out(
              self.weight
          )
          bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0
          torch.nn.init.uniform_(self.bias, -bound, bound)
  
file_path: xformers/xformers/triton/fused_linear_layer.py
imports: import math
  from typing import Any, Optional
  import torch
  import torch.nn
  from torch.cuda.amp import custom_bwd, custom_fwd
  from xformers.components.activations import Activation
  from xformers.triton.k_activations import get_triton_activation_index
  from xformers.triton.k_fused_matmul_bw import fused_matmul_backward
  from xformers.triton.k_fused_matmul_fw import fused_matmul
label_desc: Use image node_id 2 for calling the FusedLinear obj's underlying member method code with example usage: obj.reset_parameters() without return types